Département Analyse des Systèmes, Evaluation Intégrée et Modélisation

Flood disasters in Europe have become increasingly frequent and severe due to factors including rapid urbanisation, infrastructure vulnerabilities and climate change-related weather anomalies. This paper summarises lessons from major flood events since 2007 using a harmonised framework aligned with Integrated Flood Risk Management (IFRM). The analysis is based on multi-country datasets and case studies. Hazards characteristics, exposure, vulnerability and governance responses are examined in order to identify recurring patterns and systemic gaps. The results of the study highlighted five critical themes: (i) the growing prevalence of riverine and flash floods in urban areas; (ii) the amplifying effects of urbanisation and inadequate drainage; (iii) the shaping of hazard profiles by climate-induced extremes; (iv) the persistent shortcomings in the dissemination of early warnings and last-mile communication and (v) the regional variability in vulnerability and coping capacity. The insights gathered have informed a series of recommendations for policy and practice, the enhancement of early warning systems, investment in sustainable infrastructure, cross-border cooperation and community resilience. The objective of these findings is to inform adaptive strategies and strengthen Europe’s capacity to manage evolving flood risks under increasing urbanisation, continuous economic development in flood-prone areas and climate variations.

Data drives our understanding of hydrological processes, supports model development, and enables anticipatory water management. This contribution introduces EARLS: European Aggregated Reconstructions for Large-sample Studies. EARLS offers daily streamflow reconstructions for more than 10 000 basins in Europe including uncertainty estimates, covering the period from 1953 to 2023. The reconstruction is derived from a single Long Short-Term Memory (LSTM) based rainfall–runoff model trained on more than 5000 basins. LSTMs represent the state of the art in rainfall–runoff modeling and are well suited to provide predictions in ungauged basins. We evaluate the quality of the reconstruction through quantitative evaluation on two held-out sets of basins and by conducting a qualitative assessment that compares EARLS-based peak flows and flood timing to previous large-scale hydrological studies. EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data.

The complex composition of hydrological systems, climates and landscapes makes it challenging to explain and predict hydrological streamflow response. Many previous large-sample studies, mostly focused on the United States, identified climate as the primary control, with landscape exerting only a minor role in shaping hydrological behaviour. Yet, a few other studies report contradictory results with landscape being a more dominant driver. In this study, we use an unprecedented sample of more than 7000 catchments in Europe from the EStreams dataset to identify and map functionally similar catchments, together with their spatially variable climate and landscape controls. The wide spatial and temporal gradient of the study catchments was used to identify hydrological response types (HRTs) based on 40 hydrological streamflow signatures related to long-term averages and inter-annual variability of magnitude, timing, duration, frequency, and seasonality. Overall, 10 HRTs could be identified. Several HRTs are well defined and well distinguishable, largely due to catchments with strongly seasonal or more extreme behaviour. Other HRTs remain difficult to distinguish, as these catchments represent more transitional conditions with increasingly overlapping characteristics between HRTs. The underlying drivers of the HRTs were identified by using 84 climate- and landscape attributes to predict catchment membership to their respective HRT with a Random Forest classification model. Climate emerges as the dominant driver of hydrological behaviour at the continental scale. However, landscape was found, in 4 out of 10 HRTs, to be at least as strong or even stronger a control on the hydrological streamflow response. These results highlight that the complex, integrated nature of hydrological response remains challenging to disentangle, even with extensive datasets and advanced modelling approaches, and therefore, climate and landscape need to be understood as joint drivers in a co-evolutionary perspective.

Cyanobacterial blooms increasingly threaten vital freshwater ecosystems, with harmful impacts exacerbated by climate change and eutrophication. Cyanobacteria produce toxic metabolites whose concentrations increase with biomass accumulation, posing direct risks to drinking water, aquatic ecosystems and recreational use while generating substantial economic costs. Despite extensive research on temperature and nutrient effects, our explanatory and predictive capacity remains limited. We propose that this limitation stems from insufficient understanding of how biotic and abiotic factors interact to modify cyanobacterial net growth and, consequently, biomass accumulation. Here, using five years of daily monitoring data from a eutrophic lake and causal inference via state-space reconstruction modelling, we show that interactions with co-occurring plankton taxa fundamentally reshape the realized niche of bloom-forming cyanobacteria over environmental gradients. Biotic interactions shift temperature boundaries for net growth by up to 13 °C and phosphorus requirements by over 20 μg l⁻¹ in toxic Microcystaceae and Dolichospermum spp. Grazing inhibits bloom formation across cyanobacterial taxa, while facilitation by other phytoplankton may allow blooms at unexpectedly low temperatures and phosphate concentrations. These findings address a fundamental research gap—how biotic interactions shape realized niches in natural ecosystems—while offering practical insights for harmful algal bloom management.

Species distribution models (SDMs) are widely used to describe and predict species occurrences from environmental data, but calibration datasets are typically affected by noise and bias. We test the robustness of SDMs to common data limitations by generating ecologically realistic virtual benchmark data with a mechanistic food-web model and then systematically degrading the data quality. These datasets are used to train SDMs of different complexities, which we evaluate not only on their predictive performance, but also on their degree of overfitting and their ability to recover the true environmental response shapes.
While SDMs explicitly only capture environmental responses, species occurrences in nature are also affected by dispersal limitation and biotic interactions. To account for this, we generated virtual benchmark data with the model Streambugs , which explicitly includes biotic interactions and dispersal limitation at the catchment scale, to simulate presence absence data and responses of 129 stream macroinvertebrate taxa to eight environmental factors. We then systematically applied four common types of data-limitation scenarios: reduced sample size, missing predictors, random noise added to one predictor, and bias in the species detection process. Each type was assessed at four levels of degradation, and we additionally evaluated combined scenarios including all limitations. We compared statistical and machine-learning SDMs covering a range of model complexities for 35 taxa. More flexible models achieved the highest predictive performance under the best-case scenario but were sensitive to data degradation, exhibiting strong overfitting that leads to irregular response shapes, which did not reflect the data generating processes. In contrast, simpler models produced more consistent predictions and response shapes across scenarios. We illustrate how SDMs differ in their robustness to common data limitations and reveal trade-offs between predictive performance, overfitting and ecological interpretability, enabling informed decisions about model complexity according to data quality and study objectives.

Universal access to safe drinking water is called for under Sustainable Development Target 6.1 (SDG 6.1). Yet quantitative evidence on disease reductions associated with achieving SDG 6.1 and the service dimensions that most effectively reduce diarrhoeal diseases remains limited and inconsistent. Here, using multiple indicator cluster survey data from 24 low- and middle-income countries, we show that household use of safely managed drinking water services (SMDWS) reduces diarrhoea risks in children under 5 years old compared with use of services not fulfilling all of the SMDWS criteria. Drinking water availability when needed and absence of faecal contamination at the point of use showed consistent protective associations with child diarrhoea. We observed less evidence of the protective effect of an improved drinking water source, access on premises and absence of faecal contamination at the point of collection. Our findings reaffirm the importance of SMDWS in protecting against child diarrhoea, while highlighting uncertainties in estimates of the magnitude of the disease burden, which could be prevented by achieving SDG 6.1.